RESEARCH: PARKINSONS
FOLDING PROJECT #17760 PROFILE
PROJECT TEAM
Manager(s): Matthew ChanInstitution: University of Illinois at Urbana-Champaign
WORK UNIT INFO
Atoms: 65,711Core: OPENMM_22
Status: Public
Related Projects
TLDR; PROJECT SUMMARY AI BETA
This project uses computer simulations to study the serotonin transporter protein. Serotonin affects mood, sleep, and other functions, and problems with this transporter can lead to mental health disorders. The project aims to understand how changes in the transporter's structure affect its function and potentially help develop new treatments for these disorders.
Note: This TLDR is a simplication and may not be 100% accurate.OFFICAL PROJECT DESCRIPTION
These projects contains simulations of the serotonin transporter, the protein responsible for terminating neurotransmission in neurons.
The neurotransmitter serotonin regulates various functions in the body such as mood, behavior, appettite, and sleep, and is a major drug targets for antidepressants.
Malfunctions in the serotonin transporter have been assoicated with mental disorders including depression and Parkinson's.
Our goal in performing these simulations is to understand how mutations affect the structure and dynamics of the serotonin transporter and provide insights to treat psychatric disorders assoicated with these mutations.
RELATED TERMS GLOSSARY AI BETA
serotonin transporter
Protein that regulates serotonin levels in the brain.
The serotonin transporter is a protein found in neurons that moves serotonin from the synapse back into the neuron. This process is essential for regulating serotonin levels and neurotransmission. Mutations in the serotonin transporter gene can lead to imbalances in serotonin, contributing to mental health disorders like depression and Parkinson's disease.
neurotransmitter
Chemical messenger that transmits signals between neurons.
Neurotransmitters are chemicals released by neurons to communicate with each other. They play a crucial role in various brain functions, including mood regulation, movement control, and learning. Imbalances in neurotransmitter levels can contribute to mental and neurological disorders.
antidepressants
Medications used to treat depression and other mood disorders.
Antidepressants are a class of drugs that work by affecting neurotransmitter levels in the brain. They are commonly prescribed to manage symptoms of depression, anxiety, and other mental health conditions. Different types of antidepressants target specific neurotransmitters, such as serotonin or norepinephrine.
depression
Mood disorder characterized by persistent sadness and loss of interest.
Depression is a common mental health disorder that affects mood, thoughts, and behavior. People with depression may experience feelings of sadness, hopelessness, fatigue, difficulty concentrating, and changes in sleep and appetite. Treatment often involves therapy, medication, or a combination of both.
Parkinson's
Progressive neurodegenerative disorder affecting movement.
Parkinson's disease is a chronic and progressive brain disorder that primarily affects movement. Symptoms include tremors, stiffness, slow movements, and difficulty with balance. The disease is caused by the loss of dopamine-producing neurons in the brain.
mutations
Changes in the DNA sequence.
Mutations are alterations in the genetic code (DNA). They can be inherited or occur spontaneously. Mutations can have various effects on an organism, ranging from being harmless to causing disease.
psychatric
Relating to mental health.
Psychiatric refers to the field of medicine concerned with mental disorders. Psychiatrists are medical doctors who specialize in diagnosing and treating mental health conditions.
PROJECT FOLDING PPD AVERAGES BY GPU
Data as of Sunday, 26 April 2026 00:35:59|
Rank Project |
Model Name Folding@Home Identifier |
Make Brand |
GPU Model |
PPD Average |
Points WU Average |
WUs Day Average |
WU Time Average |
|---|---|---|---|---|---|---|---|
| 1 | GeForce RTX 3090 GA102 [GeForce RTX 3090] |
Nvidia | GA102 | 7,771,017 | 176,348 | 44.07 | 0 hrs 33 mins |
| 2 | GeForce RTX 3080 Lite Hash Rate GA102 [GeForce RTX 3080 Lite Hash Rate] |
Nvidia | GA102 | 7,250,791 | 171,882 | 42.18 | 0 hrs 34 mins |
| 3 | GeForce RTX 3080 Ti GA102 [GeForce RTX 3080 Ti] |
Nvidia | GA102 | 7,037,939 | 169,289 | 41.57 | 0 hrs 35 mins |
| 4 | GeForce RTX 3080 GA102 [GeForce RTX 3080] |
Nvidia | GA102 | 5,731,905 | 159,219 | 36.00 | 0 hrs 40 mins |
| 5 | GeForce RTX 2080 Ti Rev. A TU102 [GeForce RTX 2080 Ti Rev. A] M 13448 |
Nvidia | TU102 | 4,571,613 | 147,643 | 30.96 | 0 hrs 47 mins |
| 6 | GeForce RTX 2080 Ti TU102 [GeForce RTX 2080 Ti] M 13448 |
Nvidia | TU102 | 4,551,542 | 147,504 | 30.86 | 0 hrs 47 mins |
| 7 | GeForce RTX 2080 Rev. A TU104 [GeForce RTX 2080 Rev. A] 10068 |
Nvidia | TU104 | 4,293,511 | 145,312 | 29.55 | 0 hrs 49 mins |
| 8 | GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti] |
Nvidia | GA104 | 4,067,522 | 139,353 | 29.19 | 0 hrs 49 mins |
| 9 | GeForce RTX 3070 GA104 [GeForce RTX 3070] |
Nvidia | GA104 | 3,867,549 | 139,490 | 27.73 | 0 hrs 52 mins |
| 10 | GeForce RTX 2070 SUPER TU104 [GeForce RTX 2070 SUPER] 8218 |
Nvidia | TU104 | 3,394,074 | 134,527 | 25.23 | 0 hrs 57 mins |
| 11 | GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate] |
Nvidia | GA104 | 2,728,973 | 121,003 | 22.55 | 1 hrs 4 mins |
| 12 | GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380 |
Nvidia | GP102 | 2,664,883 | 124,384 | 21.42 | 1 hrs 7 mins |
| 13 | GeForce RTX 2070 Rev. A TU106 [GeForce RTX 2070 Rev. A] |
Nvidia | TU106 | 2,587,667 | 122,794 | 21.07 | 1 hrs 8 mins |
| 14 | GeForce RTX 2060 Super TU106 [GeForce RTX 2060 SUPER] |
Nvidia | TU106 | 2,138,018 | 115,050 | 18.58 | 1 hrs 17 mins |
| 15 | GeForce RTX 2060 TU104 [GeForce RTX 2060] |
Nvidia | TU104 | 2,053,252 | 114,069 | 18.00 | 1 hrs 20 mins |
| 16 | GeForce RTX 3060 Lite Hash Rate GA106 [GeForce RTX 3060 Lite Hash Rate] |
Nvidia | GA106 | 1,914,516 | 110,720 | 17.29 | 1 hrs 23 mins |
| 17 | GeForce RTX 3060 GA106 [GeForce RTX 3060] |
Nvidia | GA106 | 1,821,835 | 109,041 | 16.71 | 1 hrs 26 mins |
| 18 | GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632 |
Nvidia | GM200 | 1,514,106 | 102,024 | 14.84 | 1 hrs 37 mins |
| 19 | GeForce RTX 2060 Mobile TU106M [GeForce RTX 2060 Mobile] |
Nvidia | TU106M | 1,407,604 | 100,459 | 14.01 | 1 hrs 43 mins |
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|
|||||||
| 20 | GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873 |
Nvidia | GP104 | 1,307,748 | 97,368 | 13.43 | 1 hrs 47 mins |
| 21 | GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463 |
Nvidia | GP104 | 1,199,580 | 95,159 | 12.61 | 1 hrs 54 mins |
| 22 | GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER] |
Nvidia | TU116 | 897,390 | 85,903 | 10.45 | 2 hrs 18 mins |
| 23 | GeForce GTX 970 GM204 [GeForce GTX 970] 3494 |
Nvidia | GM204 | 504,113 | 71,226 | 7.08 | 3 hrs 23 mins |
| 24 | GeForce GTX 1060 3GB GP106 [GeForce GTX 1060 3GB] 3935 |
Nvidia | GP106 | 497,166 | 70,731 | 7.03 | 3 hrs 25 mins |
| 25 | GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138 |
Nvidia | GP107 | 357,039 | 63,690 | 5.61 | 4 hrs 17 mins |
| 26 | GeForce GTX 1650 TU117 [GeForce GTX 1650] |
Nvidia | TU117 | 284,605 | 55,302 | 5.15 | 4 hrs 40 mins |
| 27 | Quadro K1200 GM107GL [Quadro K1200] |
Nvidia | GM107GL | 56,673 | 34,401 | 1.65 | 14 hrs 34 mins |
PROJECT FOLDING PPD AVERAGES BY CPU BETA
Data as of Sunday, 26 April 2026 00:35:59|
Rank Project |
CPU Model |
Logical Processors (LP) |
PPD-PLP AVG PPD per 1 LP |
ALL LP-PPD (Estimated) |
Make |
|---|